{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BP</th>\n",
       "      <th>Age</th>\n",
       "      <th>Weight</th>\n",
       "      <th>BSA</th>\n",
       "      <th>Dur</th>\n",
       "      <th>Pulse</th>\n",
       "      <th>Stress</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>105</td>\n",
       "      <td>47</td>\n",
       "      <td>85.4</td>\n",
       "      <td>1.75</td>\n",
       "      <td>5.1</td>\n",
       "      <td>63</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>115</td>\n",
       "      <td>49</td>\n",
       "      <td>94.2</td>\n",
       "      <td>2.10</td>\n",
       "      <td>3.8</td>\n",
       "      <td>70</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>116</td>\n",
       "      <td>49</td>\n",
       "      <td>95.3</td>\n",
       "      <td>1.98</td>\n",
       "      <td>8.2</td>\n",
       "      <td>72</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>117</td>\n",
       "      <td>50</td>\n",
       "      <td>94.7</td>\n",
       "      <td>2.01</td>\n",
       "      <td>5.8</td>\n",
       "      <td>73</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>112</td>\n",
       "      <td>51</td>\n",
       "      <td>89.4</td>\n",
       "      <td>1.89</td>\n",
       "      <td>7.0</td>\n",
       "      <td>72</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    BP  Age  Weight   BSA  Dur  Pulse  Stress\n",
       "0  105   47    85.4  1.75  5.1     63      33\n",
       "1  115   49    94.2  2.10  3.8     70      14\n",
       "2  116   49    95.3  1.98  8.2     72      10\n",
       "3  117   50    94.7  2.01  5.8     73      99\n",
       "4  112   51    89.4  1.89  7.0     72      95"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import pandas\n",
    "import pandas as pd\n",
    " \n",
    "# Read the blood pressure dataset\n",
    "data = pd.read_csv(\"bloodpress.txt\",sep='\\t')\n",
    " \n",
    "# See the top records in the data\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import seaborn\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    " \n",
    "# Correlation matrix\n",
    "corr=data.corr()\n",
    "\n",
    "# Plot Heatmap on correlation matrix \n",
    "sns.heatmap(corr, annot=True, cmap='YlGnBu')\n",
    "\n",
    "# display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender\n",
       "0      F\n",
       "1      M\n",
       "2      M\n",
       "3      F\n",
       "4      M"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import pandas module\n",
    "import pandas as pd\n",
    " \n",
    "# Create pandas DataFrame\n",
    "data=pd.DataFrame({'Gender':['F','M','M','F','M']})\n",
    " \n",
    "# Check the top-5 records\n",
    "data.head()\n",
    "\n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   F  M\n",
       "0  1  0\n",
       "1  0  1\n",
       "2  0  1\n",
       "3  1  0\n",
       "4  0  1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dummy encoding\n",
    "encoded_data = pd.get_dummies(data['Gender'])\n",
    " \n",
    "# Check the top-5 records of the dataframe\n",
    "encoded_data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   M\n",
       "0  0\n",
       "1  1\n",
       "2  1\n",
       "3  0\n",
       "4  1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dummy encoding\n",
    "encoded_data = pd.get_dummies(data['Gender'], drop_first=True)\n",
    " \n",
    "# Check the top-5 records of the dataframe\n",
    "encoded_data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      TV  Radio  Newspaper  Sales\n",
       "0  230.1   37.8       69.2   22.1\n",
       "1   44.5   39.3       45.1   10.4\n",
       "2   17.2   45.9       69.3    9.3\n",
       "3  151.5   41.3       58.5   18.5\n",
       "4  180.8   10.8       58.4   12.9"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import pandas \n",
    "import pandas as pd\n",
    " \n",
    "# Read the dataset using read_csv method\n",
    "df = pd.read_csv(\"Advertising.csv\")\n",
    " \n",
    "# See the top-5 records in the data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Independent variables or Features\n",
    "X = df[['TV', 'Radio', 'Newspaper']] \n",
    " \n",
    "# Dependent or Target variable\n",
    "y = df.Sales \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import train_test_split function from \n",
    "from sklearn.model_selection import train_test_split\n",
    " \n",
    "# Split X and y into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.25,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.89257005115115\n",
      "[0.04416235 0.19900368 0.00116268]\n"
     ]
    }
   ],
   "source": [
    "# Import linear regression model\n",
    "from sklearn.linear_model import LinearRegression\n",
    " \n",
    "# Create linear regression model\n",
    "lin_reg = LinearRegression()\n",
    " \n",
    "# Fit the linear regression model\n",
    "lin_reg.fit(X_train, y_train)\n",
    " \n",
    "# Predict the values given test set\n",
    "predictions = lin_reg.predict(X_test)\n",
    "\n",
    "# Print the intercept and coefficients\n",
    "print(lin_reg.intercept_)\n",
    "print(lin_reg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error(MAE): 1.3000320919235455\n",
      "Mean Squared Error(MSE): 4.012497522917101\n",
      "Root Mean Squared Error(RMSE): 2.0031219440955415\n",
      "R2-Square: 0.8576396745320892\n"
     ]
    }
   ],
   "source": [
    "# Import the required libraries\n",
    "import numpy as np\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    " \n",
    "# Evaluate mean absolute error\n",
    "print('Mean Absolute Error(MAE):', mean_absolute_error(y_test,predictions))  \n",
    " \n",
    "# Evaluate mean squared error\n",
    "print(\"Mean Squared Error(MSE):\", mean_squared_error(y_test, predictions))  \n",
    " \n",
    "# Evaluate root mean squared error\n",
    "print(\"Root Mean Squared Error(RMSE):\", np.sqrt(mean_squared_error(y_test, predictions)))\n",
    " \n",
    "# Evaluate R2-square\n",
    "print(\"R2-Square:\",r2_score(y_test, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Polynomial Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'y-Axis')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Create X and Y lists\n",
    "X=[1,2,3,4,5,6,7,8,9,10]\n",
    "y=[9,10,12,16,22,28,40,58,102,200]\n",
    "\n",
    "# Plot scatter diagram\n",
    "plt.scatter(X,y, color = 'red')\n",
    "plt.title('Polynomial Regression')\n",
    "plt.xlabel('X-Axis')\n",
    "plt.ylabel('y-Axis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'y-Axis')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import libraries\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# Preprare dataset\n",
    "data = pd.DataFrame({\"X\":[1,2,3,4,5,6,7,8,9,10],\n",
    "                     \"y\":[9,10,12,16,22,28,40,58,102,200]})\n",
    "X = data[['X']]\n",
    "y = data[['y']]\n",
    "\n",
    "# Apply Polynomial Features\n",
    "polynomial_reg = PolynomialFeatures(degree = 6)\n",
    "X_polynomial = polynomial_reg.fit_transform(X)\n",
    "\n",
    "# Apply Linear Regression Model\n",
    "linear_reg = LinearRegression()\n",
    "linear_reg.fit(X_polynomial, y)\n",
    "predictions=linear_reg.predict(X_polynomial)\n",
    "\n",
    "# plot the results\n",
    "plt.scatter(X,y, color = 'red')\n",
    "plt.plot(X, predictions, color = 'blue')\n",
    "plt.title('Polynomial Regression')\n",
    "plt.xlabel('X-Axis')\n",
    "plt.ylabel('y-Axis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'y-Axis')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X,y, color = 'red')\n",
    "plt.plot(X, predictions, color = 'red')\n",
    "plt.title('Polynomial Regression')\n",
    "plt.xlabel('X-Axis')\n",
    "plt.ylabel('y-Axis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnant</th>\n",
       "      <th>glucose</th>\n",
       "      <th>bp</th>\n",
       "      <th>skin</th>\n",
       "      <th>insulin</th>\n",
       "      <th>bmi</th>\n",
       "      <th>pedigree</th>\n",
       "      <th>age</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnant  glucose  bp  skin  insulin   bmi  pedigree  age  label\n",
       "0         6      148  72    35        0  33.6     0.627   50      1\n",
       "1         1       85  66    29        0  26.6     0.351   31      0\n",
       "2         8      183  64     0        0  23.3     0.672   32      1\n",
       "3         1       89  66    23       94  28.1     0.167   21      0\n",
       "4         0      137  40    35      168  43.1     2.288   33      1"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import libraries\n",
    "import pandas as pd\n",
    " \n",
    "# read the dataset\n",
    "diabetes = pd.read_csv(\"diabetes.csv\")\n",
    " \n",
    "# Show top 5-records\n",
    "diabetes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split dataset in two parts: feature set and target label \n",
    "feature_set = ['pregnant', 'insulin', 'bmi', 'age','glucose','bp','pedigree']\n",
    "features = diabetes[feature_set] \n",
    "target = diabetes.label\n",
    "\n",
    "# partition data into training and testing set \n",
    "from sklearn.model_selection import train_test_split\n",
    "feature_train, feature_test, target_train, target_test = train_test_split(features, target, test_size=0.3, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Model Accuracy: 0.7835497835497836\n"
     ]
    }
   ],
   "source": [
    "# import logistic regression scikit-learn model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score # for performance evaluation\n",
    " \n",
    "# instantiate the model\n",
    "logreg = LogisticRegression(solver='lbfgs')\n",
    " \n",
    "# fit the model with data\n",
    "logreg.fit(feature_train,target_train)\n",
    " \n",
    "# Forecast the target variable for given test dataset\n",
    "predictions = logreg.predict(feature_test)\n",
    " \n",
    "# Assess model performance using accuracy measure\n",
    "print(\"Logistic Regression Model Accuracy:\",accuracy_score(target_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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